A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

S. Dey, A. Bhattacharya, A. C. Frery, C. López-Martínez and Y. S. Rao, “A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Early Access, 2021

➲ Full paper


Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model-based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume \textit{ad hoc} scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, non-negative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties.

Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers

Jacob, A., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J.M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziolkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Mallorqui, J., Duro, J. & Engdahl, M., “Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers”, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp. 535-552, 2020.

➲ Open access full paper


This work investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyses the performance of this feature along with polarisation and intensity products according to different classification strategies and algorithms. Seven different classification work flows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain: interferometric coherence, backscattered intensities and polarisation. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximise diversity land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%), obtained also promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherencebased results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinels-1A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1A/B stacks, i.e., 6-day sampling, are considered.